Overview

Dataset statistics

Number of variables29
Number of observations167622
Missing cells752
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory37.1 MiB
Average record size in memory232.0 B

Variable types

NUM19
CAT10

Warnings

total distance (km) has a high cardinality: 2659 distinct values High cardinality
average speed (km/h) has a high cardinality: 4154 distinct values High cardinality
average temperature has a high cardinality: 528 distinct values High cardinality
average wind speed (m/s) has a high cardinality: 162 distinct values High cardinality
max/min gpx height has a high cardinality: 16341 distinct values High cardinality
maxgpsheight has a high cardinality: 11857 distinct values High cardinality
average wind load (1 to -1) has a high cardinality: 182 distinct values High cardinality
avg_heart_rate_cat_org has a high cardinality: 803 distinct values High cardinality
avg_heart_rate_cat_adj has a high cardinality: 703 distinct values High cardinality
uphill8end is highly correlated with uphill/downhill hys=8 (m) and 2 other fieldsHigh correlation
uphill/downhill hys=8 (m) is highly correlated with uphill8end and 2 other fieldsHigh correlation
uphill/downhill hys=0 (m) is highly correlated with uphill/downhill hys=8 (m) and 2 other fieldsHigh correlation
uphill0end is highly correlated with uphill/downhill hys=8 (m) and 2 other fieldsHigh correlation
uphill/downhill hys=8 (m) is highly skewed (γ1 = 99.33185539) Skewed
uphill8end is highly skewed (γ1 = 104.3464029) Skewed
uphill/downhill hys=0 (m) is highly skewed (γ1 = 92.11190209) Skewed
uphill0end is highly skewed (γ1 = 96.6644515) Skewed
total real energy (kJ) is highly skewed (γ1 = 281.5546879) Skewed
total estimated energy (kJ) is highly skewed (γ1 = 31.90478045) Skewed
average power (W) is highly skewed (γ1 = 185.9572907) Skewed
average estimated power (W) is highly skewed (γ1 = 32.10429658) Skewed
normalized real power (W) is highly skewed (γ1 = 43.83014039) Skewed
normalized estimated power (W) is highly skewed (γ1 = 53.48094336) Skewed
cumulative hr intensity score is highly skewed (γ1 = -409.3680887) Skewed
df_index has unique values Unique
uphill/downhill hys=8 (m) has 11316 (6.8%) zeros Zeros
uphill8end has 11702 (7.0%) zeros Zeros
average wind direction (deg) has 65199 (38.9%) zeros Zeros
total real energy (kJ) has 96372 (57.5%) zeros Zeros
average power (W) has 96294 (57.4%) zeros Zeros
normalized real power (W) has 96326 (57.5%) zeros Zeros
number_of_participants has 28050 (16.7%) zeros Zeros
avg slope length (m) has 73348 (43.8%) zeros Zeros

Reproduction

Analysis started2021-10-14 01:11:19.960149
Analysis finished2021-10-14 01:13:11.199975
Duration1 minute and 51.24 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct167622
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83810.5
Minimum0
Maximum167621
Zeros1
Zeros (%)< 0.1%
Memory size1.3 MiB
2021-10-14T03:13:11.383485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8381.05
Q141905.25
median83810.5
Q3125715.75
95-th percentile159239.95
Maximum167621
Range167621
Interquartile range (IQR)83810.5

Descriptive statistics

Standard deviation48388.44775
Coefficient of variation (CV)0.5773554358
Kurtosis-1.2
Mean83810.5
Median Absolute Deviation (MAD)41905.5
Skewness-1.516506131e-17
Sum1.404848363e+10
Variance2341441876
MonotocityNot monotonic
2021-10-14T03:13:11.551038image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01< 0.1%
 
1236251< 0.1%
 
48231< 0.1%
 
273521< 0.1%
 
253051< 0.1%
 
314501< 0.1%
 
294031< 0.1%
 
191641< 0.1%
 
171171< 0.1%
 
232621< 0.1%
 
Other values (167612)167612> 99.9%
 
ValueCountFrequency (%) 
01< 0.1%
 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
1676211< 0.1%
 
1676201< 0.1%
 
1676191< 0.1%
 
1676181< 0.1%
 
1676171< 0.1%
 

user-id
Real number (ℝ≥0)

Distinct1437
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4236.598394
Minimum1
Maximum8210
Zeros0
Zeros (%)0.0%
Memory size1.3 MiB
2021-10-14T03:13:11.730557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1591
Q11919
median2798
Q37563
95-th percentile8122
Maximum8210
Range8209
Interquartile range (IQR)5644

Descriptive statistics

Standard deviation2658.458945
Coefficient of variation (CV)0.6274984546
Kurtosis-1.526379073
Mean4236.598394
Median Absolute Deviation (MAD)1204
Skewness0.3608078996
Sum710147096
Variance7067403.962
MonotocityNot monotonic
2021-10-14T03:13:11.884146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
799230881.8%
 
177018641.1%
 
766717821.1%
 
756315270.9%
 
788314120.8%
 
763313870.8%
 
762612420.7%
 
806512280.7%
 
816112080.7%
 
175512020.7%
 
Other values (1427)15168290.5%
 
ValueCountFrequency (%) 
12< 0.1%
 
64490.3%
 
74060.2%
 
132880.2%
 
161880.1%
 
ValueCountFrequency (%) 
82101100.1%
 
820714< 0.1%
 
81997460.4%
 
81917740.5%
 
8186980.1%
 

m/v
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
M
151750 
F
15872 
ValueCountFrequency (%) 
M15175090.5%
 
F158729.5%
 
2021-10-14T03:13:12.031752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-10-14T03:13:12.119001image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:13:12.216740image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

weight
Real number (ℝ≥0)

Distinct80
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.81507797
Minimum0
Maximum267
Zeros658
Zeros (%)0.4%
Memory size1.3 MiB
2021-10-14T03:13:12.663545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile59
Q168
median75
Q383
95-th percentile95
Maximum267
Range267
Interquartile range (IQR)15

Descriptive statistics

Standard deviation13.20316512
Coefficient of variation (CV)0.1741495949
Kurtosis13.26068358
Mean75.81507797
Median Absolute Deviation (MAD)7
Skewness0.855320527
Sum12708275
Variance174.3235691
MonotocityNot monotonic
2021-10-14T03:13:12.826111image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
6885445.1%
 
8581344.9%
 
8180824.8%
 
7576584.6%
 
7075714.5%
 
7268464.1%
 
8064243.8%
 
7862773.7%
 
8460773.6%
 
7158763.5%
 
Other values (70)9613357.4%
 
ValueCountFrequency (%) 
06580.4%
 
4543< 0.1%
 
4951< 0.1%
 
506< 0.1%
 
5112770.8%
 
ValueCountFrequency (%) 
2672< 0.1%
 
2145< 0.1%
 
20556< 0.1%
 
18019< 0.1%
 
1722680.2%
 

age
Real number (ℝ≥0)

Distinct63
Distinct (%)< 0.1%
Missing752
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean41.40496794
Minimum0
Maximum76
Zeros33
Zeros (%)< 0.1%
Memory size1.3 MiB
2021-10-14T03:13:12.985684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24
Q134
median41
Q349
95-th percentile62
Maximum76
Range76
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.79401958
Coefficient of variation (CV)0.2606938278
Kurtosis-0.1306070381
Mean41.40496794
Median Absolute Deviation (MAD)7
Skewness0.2168545966
Sum6909247
Variance116.5108587
MonotocityNot monotonic
2021-10-14T03:13:13.144298image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3095565.7%
 
3989855.4%
 
4484105.0%
 
4577354.6%
 
3674564.4%
 
3366574.0%
 
5065093.9%
 
4063463.8%
 
3862893.8%
 
4354523.3%
 
Other values (53)9347555.8%
 
ValueCountFrequency (%) 
033< 0.1%
 
142< 0.1%
 
151460.1%
 
162540.2%
 
1710070.6%
 
ValueCountFrequency (%) 
7647< 0.1%
 
7413< 0.1%
 
733160.2%
 
7237< 0.1%
 
711< 0.1%
 

length
Real number (ℝ≥0)

Distinct60
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean179.2729833
Minimum0
Maximum248
Zeros658
Zeros (%)0.4%
Memory size1.3 MiB
2021-10-14T03:13:13.309818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile165
Q1175
median180
Q3186
95-th percentile192
Maximum248
Range248
Interquartile range (IQR)11

Descriptive statistics

Standard deviation14.31349823
Coefficient of variation (CV)0.07984191468
Kurtosis96.74006834
Mean179.2729833
Median Absolute Deviation (MAD)6
Skewness-8.019146739
Sum30050096
Variance204.8762317
MonotocityNot monotonic
2021-10-14T03:13:13.478367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
180141628.4%
 
178129957.8%
 
18891365.5%
 
19184915.1%
 
17974554.4%
 
18672984.4%
 
17671894.3%
 
17771664.3%
 
18571074.2%
 
17068164.1%
 
Other values (50)7980747.6%
 
ValueCountFrequency (%) 
06580.4%
 
6053< 0.1%
 
6934< 0.1%
 
10419< 0.1%
 
10554< 0.1%
 
ValueCountFrequency (%) 
24856< 0.1%
 
20856< 0.1%
 
2052320.1%
 
2031260.1%
 
20261< 0.1%
 

total distance (km)
Categorical

HIGH CARDINALITY

Distinct2659
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
61
 
359
60,2
 
336
60
 
333
60,1
 
328
59,9
 
324
Other values (2654)
165942 
ValueCountFrequency (%) 
613590.2%
 
60,23360.2%
 
603330.2%
 
60,13280.2%
 
59,93240.2%
 
62,63160.2%
 
62,43080.2%
 
61,43080.2%
 
55,33070.2%
 
62,93010.2%
 
Other values (2649)16440298.1%
 
2021-10-14T03:13:13.672849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique328 ?
Unique (%)0.2%
2021-10-14T03:13:13.842394image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length4
Mean length3.971274654
Min length1

average speed (km/h)
Categorical

HIGH CARDINALITY

Distinct4154
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
28,44
 
205
25,59
 
204
26,01
 
201
26,51
 
200
28,47
 
200
Other values (4149)
166612 
ValueCountFrequency (%) 
28,442050.1%
 
25,592040.1%
 
26,012010.1%
 
26,512000.1%
 
28,472000.1%
 
28,21990.1%
 
01990.1%
 
26,191950.1%
 
26,351920.1%
 
28,111900.1%
 
Other values (4144)16563798.8%
 
2021-10-14T03:13:14.010943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique341 ?
Unique (%)0.2%
2021-10-14T03:13:14.178495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length5
Mean length4.859362136
Min length1

uphill/downhill hys=8 (m)
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct3789
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean458.3105261
Minimum0
Maximum238637
Zeros11316
Zeros (%)6.8%
Memory size1.3 MiB
2021-10-14T03:13:14.316164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q138
median202
Q3605
95-th percentile1708
Maximum238637
Range238637
Interquartile range (IQR)567

Descriptive statistics

Standard deviation1211.184628
Coefficient of variation (CV)2.642716149
Kurtosis15770.52884
Mean458.3105261
Median Absolute Deviation (MAD)186
Skewness99.33185539
Sum76822927
Variance1466968.204
MonotocityNot monotonic
2021-10-14T03:13:14.492692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0113166.8%
 
851893.1%
 
937102.2%
 
1730521.8%
 
1821041.3%
 
2615790.9%
 
2513790.8%
 
2713000.8%
 
1612770.8%
 
1912260.7%
 
Other values (3779)13549080.8%
 
ValueCountFrequency (%) 
0113166.8%
 
851893.1%
 
937102.2%
 
1012220.7%
 
114280.3%
 
ValueCountFrequency (%) 
2386371< 0.1%
 
1886761< 0.1%
 
1646621< 0.1%
 
1171782< 0.1%
 
967251< 0.1%
 

uphill8end
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct3706
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean451.2794084
Minimum0
Maximum238637
Zeros11702
Zeros (%)7.0%
Memory size1.3 MiB
2021-10-14T03:13:14.652265image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q138
median203
Q3601
95-th percentile1683
Maximum238637
Range238637
Interquartile range (IQR)563

Descriptive statistics

Standard deviation1205.596267
Coefficient of variation (CV)2.671507373
Kurtosis16891.978
Mean451.2794084
Median Absolute Deviation (MAD)186
Skewness104.3464029
Sum75644357
Variance1453462.359
MonotocityNot monotonic
2021-10-14T03:13:14.825766image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0117027.0%
 
854663.3%
 
937752.3%
 
1737682.2%
 
1819541.2%
 
2517881.1%
 
2616651.0%
 
2713210.8%
 
3510870.6%
 
1010390.6%
 
Other values (3696)13405780.0%
 
ValueCountFrequency (%) 
0117027.0%
 
854663.3%
 
937752.3%
 
1010390.6%
 
112760.2%
 
ValueCountFrequency (%) 
2386371< 0.1%
 
2012831< 0.1%
 
1587341< 0.1%
 
1171782< 0.1%
 
1023761< 0.1%
 

uphill/downhill hys=0 (m)
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct4131
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean663.5069203
Minimum0
Maximum238637
Zeros195
Zeros (%)0.1%
Memory size1.3 MiB
2021-10-14T03:13:14.986335image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile57
Q1200
median422
Q3867
95-th percentile2004
Maximum238637
Range238637
Interquartile range (IQR)667

Descriptive statistics

Standard deviation1241.494926
Coefficient of variation (CV)1.871110742
Kurtosis14238.05815
Mean663.5069203
Median Absolute Deviation (MAD)272
Skewness92.11190209
Sum111218357
Variance1541309.651
MonotocityNot monotonic
2021-10-14T03:13:15.148901image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1983720.2%
 
1443280.2%
 
1723240.2%
 
3063120.2%
 
1603080.2%
 
1833000.2%
 
1342970.2%
 
1482960.2%
 
1772950.2%
 
1422940.2%
 
Other values (4121)16449698.1%
 
ValueCountFrequency (%) 
01950.1%
 
123< 0.1%
 
249< 0.1%
 
369< 0.1%
 
41130.1%
 
ValueCountFrequency (%) 
2386371< 0.1%
 
1886761< 0.1%
 
1647181< 0.1%
 
1174272< 0.1%
 
967291< 0.1%
 

uphill0end
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct4035
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean656.7462684
Minimum0
Maximum238637
Zeros265
Zeros (%)0.2%
Memory size1.3 MiB
2021-10-14T03:13:15.321440image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile58
Q1200
median421
Q3864
95-th percentile1983
Maximum238637
Range238637
Interquartile range (IQR)664

Descriptive statistics

Standard deviation1236.108261
Coefficient of variation (CV)1.882170208
Kurtosis15232.38209
Mean656.7462684
Median Absolute Deviation (MAD)271
Skewness96.6644515
Sum110085123
Variance1527963.633
MonotocityNot monotonic
2021-10-14T03:13:15.489991image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1823330.2%
 
1463190.2%
 
1503190.2%
 
2003170.2%
 
2183160.2%
 
1563130.2%
 
2083080.2%
 
1453030.2%
 
1832980.2%
 
1322980.2%
 
Other values (4025)16449898.1%
 
ValueCountFrequency (%) 
02650.2%
 
147< 0.1%
 
265< 0.1%
 
378< 0.1%
 
41020.1%
 
ValueCountFrequency (%) 
2386371< 0.1%
 
2012831< 0.1%
 
1587621< 0.1%
 
1174352< 0.1%
 
1023911< 0.1%
 

average temperature
Categorical

HIGH CARDINALITY

Distinct528
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
28986 
18,1
 
786
18,4
 
778
17,7
 
752
17
 
746
Other values (523)
135574 
ValueCountFrequency (%) 
02898617.3%
 
18,17860.5%
 
18,47780.5%
 
17,77520.4%
 
177460.4%
 
19,37350.4%
 
17,47300.4%
 
20,67280.4%
 
17,87240.4%
 
18,37240.4%
 
Other values (518)13193378.7%
 
2021-10-14T03:13:15.678484image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique43 ?
Unique (%)< 0.1%
2021-10-14T03:13:15.839055image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length4
Mean length3.149377767
Min length1

average wind speed (m/s)
Categorical

HIGH CARDINALITY

Distinct162
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
65222 
2
 
2582
1,9
 
2569
2,1
 
2474
1,7
 
2432
Other values (157)
92343 
ValueCountFrequency (%) 
06522238.9%
 
225821.5%
 
1,925691.5%
 
2,124741.5%
 
1,724321.5%
 
2,624191.4%
 
1,624161.4%
 
1,524091.4%
 
1,823871.4%
 
2,523221.4%
 
Other values (152)8039048.0%
 
2021-10-14T03:13:16.023599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique13 ?
Unique (%)< 0.1%
2021-10-14T03:13:16.190117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length3
Mean length2.100702772
Min length1

average wind direction (deg)
Real number (ℝ≥0)

ZEROS

Distinct360
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.9212574
Minimum0
Maximum359
Zeros65199
Zeros (%)38.9%
Memory size1.3 MiB
2021-10-14T03:13:16.344704image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median76
Q3216
95-th percentile311
Maximum359
Range359
Interquartile range (IQR)216

Descriptive statistics

Standard deviation114.1309145
Coefficient of variation (CV)1.028936358
Kurtosis-1.25104972
Mean110.9212574
Median Absolute Deviation (MAD)76
Skewness0.4777341301
Sum18592843
Variance13025.86565
MonotocityNot monotonic
2021-10-14T03:13:16.521232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
06519938.9%
 
2405200.3%
 
2215040.3%
 
2334930.3%
 
2354910.3%
 
2224870.3%
 
2304850.3%
 
2104840.3%
 
2034830.3%
 
2264810.3%
 
Other values (350)9799558.5%
 
ValueCountFrequency (%) 
06519938.9%
 
178< 0.1%
 
21000.1%
 
3880.1%
 
41180.1%
 
ValueCountFrequency (%) 
35923< 0.1%
 
35848< 0.1%
 
35763< 0.1%
 
35681< 0.1%
 
35570< 0.1%
 

max/min gpx height
Categorical

HIGH CARDINALITY

Distinct16341
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
 
6610
13
 
238
11,2
 
238
9,8
 
233
20,6
 
232
Other values (16336)
160071 
ValueCountFrequency (%) 
066103.9%
 
132380.1%
 
11,22380.1%
 
9,82330.1%
 
20,62320.1%
 
10,82320.1%
 
102310.1%
 
142200.1%
 
62190.1%
 
9,62190.1%
 
Other values (16331)15895094.8%
 
2021-10-14T03:13:16.783532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique5150 ?
Unique (%)3.1%
2021-10-14T03:13:16.953079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length4
Mean length4.111441219
Min length1

maxgpsheight
Categorical

HIGH CARDINALITY

Distinct11857
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
 
1303
-3
 
655
-2
 
626
-0,2
 
616
1
 
600
Other values (11852)
163822 
ValueCountFrequency (%) 
013030.8%
 
-36550.4%
 
-26260.4%
 
-0,26160.4%
 
16000.4%
 
-15900.4%
 
-0,85820.3%
 
-2,45820.3%
 
-0,65720.3%
 
0,45510.3%
 
Other values (11847)16094596.0%
 
2021-10-14T03:13:17.140578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3865 ?
Unique (%)2.3%
2021-10-14T03:13:17.298155image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length4
Mean length3.927479686
Min length1

total real energy (kJ)
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct5289
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean952.0742862
Minimum0
Maximum10508164
Zeros96372
Zeros (%)57.5%
Memory size1.3 MiB
2021-10-14T03:13:17.440773image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31545
95-th percentile2967.95
Maximum10508164
Range10508164
Interquartile range (IQR)1545

Descriptive statistics

Standard deviation36668.71405
Coefficient of variation (CV)38.51455142
Kurtosis80479.55897
Mean952.0742862
Median Absolute Deviation (MAD)0
Skewness281.5546879
Sum159588596
Variance1344594590
MonotocityNot monotonic
2021-10-14T03:13:17.612316image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
09637257.5%
 
139281< 0.1%
 
146979< 0.1%
 
148079< 0.1%
 
173374< 0.1%
 
149474< 0.1%
 
200372< 0.1%
 
156072< 0.1%
 
159271< 0.1%
 
149571< 0.1%
 
Other values (5279)7057742.1%
 
ValueCountFrequency (%) 
09637257.5%
 
128< 0.1%
 
218< 0.1%
 
39< 0.1%
 
47< 0.1%
 
ValueCountFrequency (%) 
105081642< 0.1%
 
18625481< 0.1%
 
5055493< 0.1%
 
2036061< 0.1%
 
957781< 0.1%
 

total estimated energy (kJ)
Real number (ℝ)

SKEWED

Distinct5143
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1254.472736
Minimum-1543
Maximum154850
Zeros223
Zeros (%)0.1%
Memory size1.3 MiB
2021-10-14T03:13:17.792833image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1543
5-th percentile118
Q1761
median1129
Q31616
95-th percentile2691
Maximum154850
Range156393
Interquartile range (IQR)855

Descriptive statistics

Standard deviation893.4703636
Coefficient of variation (CV)0.7122278052
Kurtosis5223.281528
Mean1254.472736
Median Absolute Deviation (MAD)418
Skewness31.90478045
Sum210277229
Variance798289.2907
MonotocityNot monotonic
2021-10-14T03:13:17.945424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
02230.1%
 
9141580.1%
 
10161580.1%
 
11161570.1%
 
10991520.1%
 
10551510.1%
 
10771500.1%
 
9421460.1%
 
8641430.1%
 
9551420.1%
 
Other values (5133)16604299.1%
 
ValueCountFrequency (%) 
-15431< 0.1%
 
-13481< 0.1%
 
-11501< 0.1%
 
-11461< 0.1%
 
-11261< 0.1%
 
ValueCountFrequency (%) 
1548501< 0.1%
 
245402< 0.1%
 
206741< 0.1%
 
173031< 0.1%
 
168192< 0.1%
 

average power (W)
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct50334
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94426.11477
Minimum0
Maximum730107252
Zeros96294
Zeros (%)57.4%
Memory size1.3 MiB
2021-10-14T03:13:18.143894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3164058.75
95-th percentile222215
Maximum730107252
Range730107252
Interquartile range (IQR)164058.75

Descriptive statistics

Standard deviation3394881.246
Coefficient of variation (CV)35.9527791
Kurtosis35824.50566
Mean94426.11477
Median Absolute Deviation (MAD)0
Skewness185.9572907
Sum1.582789421e+10
Variance1.152521867e+13
MonotocityNot monotonic
2021-10-14T03:13:18.307457image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
09629457.4%
 
16284046< 0.1%
 
16565137< 0.1%
 
20307136< 0.1%
 
20503836< 0.1%
 
16276134< 0.1%
 
20848534< 0.1%
 
21173534< 0.1%
 
22147734< 0.1%
 
20885134< 0.1%
 
Other values (50324)7100342.4%
 
ValueCountFrequency (%) 
09629457.4%
 
325< 0.1%
 
51< 0.1%
 
84< 0.1%
 
101< 0.1%
 
ValueCountFrequency (%) 
7301072522< 0.1%
 
5253926723< 0.1%
 
1880339831< 0.1%
 
212388391< 0.1%
 
131793191< 0.1%
 

average estimated power (W)
Real number (ℝ)

SKEWED

Distinct95678
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119574.6987
Minimum-643339
Maximum8690989
Zeros200
Zeros (%)0.1%
Memory size1.3 MiB
2021-10-14T03:13:18.523878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-643339
5-th percentile38738.45
Q189359.25
median116919
Q3145172.75
95-th percentile197817.1
Maximum8690989
Range9334328
Interquartile range (IQR)55813.5

Descriptive statistics

Standard deviation73182.0355
Coefficient of variation (CV)0.6120194013
Kurtosis2789.574354
Mean119574.6987
Median Absolute Deviation (MAD)27902
Skewness32.10429658
Sum2.004335014e+10
Variance5355610320
MonotocityNot monotonic
2021-10-14T03:13:18.678464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
02000.1%
 
14135224< 0.1%
 
14674423< 0.1%
 
11522521< 0.1%
 
13869421< 0.1%
 
15819821< 0.1%
 
15333921< 0.1%
 
11171621< 0.1%
 
14953620< 0.1%
 
11057120< 0.1%
 
Other values (95668)16723099.8%
 
ValueCountFrequency (%) 
-6433391< 0.1%
 
-6357282< 0.1%
 
-4281322< 0.1%
 
-3933971< 0.1%
 
-3600391< 0.1%
 
ValueCountFrequency (%) 
86909892< 0.1%
 
43742612< 0.1%
 
42477002< 0.1%
 
36415392< 0.1%
 
35709802< 0.1%
 

normalized real power (W)
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct494
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.57364188
Minimum0
Maximum18724
Zeros96326
Zeros (%)57.5%
Memory size1.3 MiB
2021-10-14T03:13:18.836043image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3186
95-th percentile243
Maximum18724
Range18724
Interquartile range (IQR)186

Descriptive statistics

Standard deviation156.9798861
Coefficient of variation (CV)1.856132509
Kurtosis3821.328527
Mean84.57364188
Median Absolute Deviation (MAD)0
Skewness43.83014039
Sum14176403
Variance24642.68464
MonotocityNot monotonic
2021-10-14T03:13:18.993666image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
09632657.5%
 
2037840.5%
 
1917610.5%
 
1827320.4%
 
1897230.4%
 
1967190.4%
 
1867170.4%
 
1987160.4%
 
2007060.4%
 
2236980.4%
 
Other values (484)6474038.6%
 
ValueCountFrequency (%) 
09632657.5%
 
138< 0.1%
 
213< 0.1%
 
32< 0.1%
 
44< 0.1%
 
ValueCountFrequency (%) 
187241< 0.1%
 
179191< 0.1%
 
138171< 0.1%
 
126391< 0.1%
 
117301< 0.1%
 

normalized estimated power (W)
Real number (ℝ≥0)

SKEWED

Distinct1098
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean218.606937
Minimum0
Maximum108703
Zeros200
Zeros (%)0.1%
Memory size1.3 MiB
2021-10-14T03:13:19.170151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile85
Q1140
median181
Q3232
95-th percentile345
Maximum108703
Range108703
Interquartile range (IQR)92

Descriptive statistics

Standard deviation1045.943502
Coefficient of variation (CV)4.784585138
Kurtosis3343.9457
Mean218.606937
Median Absolute Deviation (MAD)45
Skewness53.48094336
Sum36643332
Variance1093997.809
MonotocityNot monotonic
2021-10-14T03:13:19.337702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
16811160.7%
 
17210930.7%
 
15510850.6%
 
16310810.6%
 
15410790.6%
 
16410700.6%
 
18210600.6%
 
15610540.6%
 
16510530.6%
 
16710530.6%
 
Other values (1088)15687893.6%
 
ValueCountFrequency (%) 
02000.1%
 
22< 0.1%
 
47< 0.1%
 
59< 0.1%
 
611< 0.1%
 
ValueCountFrequency (%) 
1087031< 0.1%
 
892842< 0.1%
 
803632< 0.1%
 
736742< 0.1%
 
561312< 0.1%
 

number_of_participants
Real number (ℝ≥0)

ZEROS

Distinct219
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.202372004
Minimum0
Maximum452
Zeros28050
Zeros (%)16.7%
Memory size1.3 MiB
2021-10-14T03:13:19.511240image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile11
Maximum452
Range452
Interquartile range (IQR)1

Descriptive statistics

Standard deviation9.716306017
Coefficient of variation (CV)3.034096603
Kurtosis279.1106459
Mean3.202372004
Median Absolute Deviation (MAD)1
Skewness13.31443745
Sum536788
Variance94.40660261
MonotocityNot monotonic
2021-10-14T03:13:19.666822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
18076048.2%
 
02805016.7%
 
22152412.8%
 
381884.9%
 
449993.0%
 
537892.3%
 
628811.7%
 
724151.4%
 
820491.2%
 
918301.1%
 
Other values (209)111376.6%
 
ValueCountFrequency (%) 
02805016.7%
 
18076048.2%
 
22152412.8%
 
381884.9%
 
449993.0%
 
ValueCountFrequency (%) 
4521< 0.1%
 
3831< 0.1%
 
3451< 0.1%
 
3301< 0.1%
 
3171< 0.1%
 

average wind load (1 to -1)
Categorical

HIGH CARDINALITY

Distinct182
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
38896 
-0,01
22063 
0,01
21543 
0,02
10445 
-0,02
10401 
Other values (177)
64274 
ValueCountFrequency (%) 
03889623.2%
 
-0,012206313.2%
 
0,012154312.9%
 
0,02104456.2%
 
-0,02104016.2%
 
0,0358633.5%
 
-0,0357463.4%
 
0,0438872.3%
 
-0,0436702.2%
 
0,0528481.7%
 
Other values (172)4226025.2%
 
2021-10-14T03:13:19.858310image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique4 ?
Unique (%)< 0.1%
2021-10-14T03:13:20.031847image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length4
Mean length3.661935784
Min length1

cumulative hr intensity score
Real number (ℝ)

SKEWED

Distinct85430
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-126620.3839
Minimum-3.214664261e+10
Maximum211992640
Zeros199
Zeros (%)0.1%
Memory size1.3 MiB
2021-10-14T03:13:20.220343image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.214664261e+10
5-th percentile6904
Q125885.25
median44718.5
Q372208
95-th percentile158584.45
Maximum211992640
Range3.235863525e+10
Interquartile range (IQR)46322.75

Descriptive statistics

Standard deviation78521415.88
Coefficient of variation (CV)-620.1325054
Kurtosis167595.5574
Mean-126620.3839
Median Absolute Deviation (MAD)21830.5
Skewness-409.3680887
Sum-2.122436198e+10
Variance6.165612751e+15
MonotocityNot monotonic
2021-10-14T03:13:20.382908image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01990.1%
 
5658615< 0.1%
 
8347714< 0.1%
 
1342114< 0.1%
 
3640613< 0.1%
 
2589513< 0.1%
 
3078412< 0.1%
 
3578012< 0.1%
 
2852612< 0.1%
 
357312< 0.1%
 
Other values (85420)16730699.8%
 
ValueCountFrequency (%) 
-3.214664261e+101< 0.1%
 
-677824722< 0.1%
 
-10483881< 0.1%
 
01990.1%
 
1032< 0.1%
 
ValueCountFrequency (%) 
2119926401< 0.1%
 
816286342< 0.1%
 
461557973< 0.1%
 
361282171< 0.1%
 
333132901< 0.1%
 

avg_heart_rate_cat_org
Categorical

HIGH CARDINALITY

Distinct803
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
9
 
4019
1
 
3168
2
 
601
3,96
 
468
4,22
 
446
Other values (798)
158920 
ValueCountFrequency (%) 
940192.4%
 
131681.9%
 
26010.4%
 
3,964680.3%
 
4,224460.3%
 
4,084440.3%
 
4,14420.3%
 
3,94360.3%
 
4,124360.3%
 
3,794350.3%
 
Other values (793)15672793.5%
 
2021-10-14T03:13:20.582375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2021-10-14T03:13:20.747932image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length4
Mean length3.745665843
Min length1

avg_heart_rate_cat_adj
Categorical

HIGH CARDINALITY

Distinct703
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2
 
5191
9
 
4022
2,01
 
598
2,02
 
533
3,19
 
507
Other values (698)
156771 
ValueCountFrequency (%) 
251913.1%
 
940222.4%
 
2,015980.4%
 
2,025330.3%
 
3,195070.3%
 
2,995060.3%
 
2,975000.3%
 
3,055000.3%
 
3,214990.3%
 
2,044960.3%
 
Other values (693)15427092.0%
 
2021-10-14T03:13:20.919512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2021-10-14T03:13:21.079085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length4
Mean length3.721396953
Min length1

avg slope length (m)
Real number (ℝ≥0)

ZEROS

Distinct5146
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2297.915214
Minimum0
Maximum180700
Zeros73348
Zeros (%)43.8%
Memory size1.3 MiB
2021-10-14T03:13:21.228647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1200
Q32950
95-th percentile8550
Maximum180700
Range180700
Interquartile range (IQR)2950

Descriptive statistics

Standard deviation4375.183206
Coefficient of variation (CV)1.903979389
Kurtosis149.8999663
Mean2297.915214
Median Absolute Deviation (MAD)1200
Skewness8.113448453
Sum385181144
Variance19142228.09
MonotocityNot monotonic
2021-10-14T03:13:21.382236image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
07334843.8%
 
110020521.2%
 
120018351.1%
 
130016261.0%
 
100015170.9%
 
140014320.9%
 
160012640.8%
 
150012390.7%
 
180011360.7%
 
170010760.6%
 
Other values (5136)8109748.4%
 
ValueCountFrequency (%) 
07334843.8%
 
1001260.1%
 
1301< 0.1%
 
1332< 0.1%
 
1471< 0.1%
 
ValueCountFrequency (%) 
1807001< 0.1%
 
1804001< 0.1%
 
1589001< 0.1%
 
1577001< 0.1%
 
1373001< 0.1%
 

Interactions

2021-10-14T03:11:43.724225image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:11:44.059203image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:11:44.279426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:11:44.488909image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:11:44.719283image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:11:44.933235image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:11:45.152673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:11:45.370092image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:11:45.582525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:11:45.789968image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:11:46.004355image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:11:46.209767image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:11:46.426663image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-10-14T03:12:37.110468image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:37.332911image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:37.528388image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:37.731844image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:37.932305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:38.133735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:38.336193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:38.549622image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:38.757103image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:38.979474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:39.190906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:39.384424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:39.597818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:39.804274image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:40.003732image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:40.216203image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:40.429596image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:40.633088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:40.825573image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:41.021056image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:41.219483image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:41.415010image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:41.618416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:41.814891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:42.016352image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:42.217813image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:42.419313image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:42.621771image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:42.836160image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:43.046600image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:43.249087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:43.460528image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:43.661992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:43.862418image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:44.092802image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:44.340140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:44.567533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:44.766999image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:44.953539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:45.160947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:45.345456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:45.529959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:45.716497image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:45.904956image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:46.098442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:46.292956image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:46.483411image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:46.676893image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:46.870376image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:47.060869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:47.259371image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:47.438856image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:47.621367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:47.815885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:48.014320image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:48.228744image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:48.426248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:48.625682image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:48.829139image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:49.027189image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:49.222704image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:49.429155image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:49.641548image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:49.839055image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:50.044472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:50.239984image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:50.449424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:50.650849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:50.847324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:51.061750image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:51.256270image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:51.450710image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:51.660150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:51.873581image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:52.317433image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:52.506886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:52.698375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:52.905858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:53.102297image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:53.285804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:53.471342image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:53.664790image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:53.868250image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:54.065718image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:54.254218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:54.448735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:54.648201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:54.839650image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:55.033177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:55.228610image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:55.410127image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:55.599654image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:55.795132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:55.988611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:56.164109image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-10-14T03:12:56.716632image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:56.905128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:57.083659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:57.263207image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:57.450706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:57.637211image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:57.818722image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:58.013165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:58.203694image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:58.379188image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:58.565689image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:58.745213image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:58.935701image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:59.128219image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-10-14T03:12:59.527152image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:59.727582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:12:59.931038image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-10-14T03:13:02.026436image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:13:02.219921image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-10-14T03:13:02.628865image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:13:02.827337image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-10-14T03:13:03.665055image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:13:03.864522image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:13:04.080982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-10-14T03:13:04.661428image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:13:04.862890image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:13:05.059368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:13:05.264781image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:13:05.464283image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-10-14T03:13:05.883126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:13:06.072659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:13:06.280064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:13:06.479531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:13:06.676008image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:13:06.880501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-10-14T03:13:21.555812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-14T03:13:21.936754image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-14T03:13:22.292834image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-14T03:13:23.079745image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-14T03:13:07.598540image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:13:09.556305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-14T03:13:10.656429image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Sample

First rows

df_indexuser-idm/vweightagelengthtotal distance (km)average speed (km/h)uphill/downhill hys=8 (m)uphill8enduphill/downhill hys=0 (m)uphill0endaverage temperatureaverage wind speed (m/s)average wind direction (deg)max/min gpx heightmaxgpsheighttotal real energy (kJ)total estimated energy (kJ)average power (W)average estimated power (W)normalized real power (W)normalized estimated power (W)number_of_participantsaverage wind load (1 to -1)cumulative hr intensity scoreavg_heart_rate_cat_orgavg_heart_rate_cat_adjavg slope length (m)
0926451748M7946.017987,626,4725826950350624,70097,6-69,4012050101376013220,01409513,323,1313800
1849761748M7946.017975,625,761781913934119,600100,2-3,6010830103139012720,01257712,242,341300
2926191748M7946.017975,625,761781913934119,600100,2-3,6010830103139012720,01257712,242,341300
3850411748M7946.017980,721,757187019118932500553,2-21187998894457519411817920,01534573,062,967900
4926841748M7946.017980,721,757187019118932500553,2-21187998894457519411817920,01534573,062,967900
5850581748M7946.017987,525,2227626052350921,400126,612,4169812171362339763415217120,01760204,454,262400
6927011748M7946.017987,525,2227626052350921,400126,612,4169812171362339763415217120,01760204,454,262400
7849791748M7946.017974,425,61661763914042,9001035,301045099881013410,02284733,22,951400
8926221748M7946.017974,425,61661763914042,9001035,301045099881013410,02284733,22,951400
9850091748M7946.01797223,5814415433833825,700100,93,4084407679109510,02236202,692,541300

Last rows

df_indexuser-idm/vweightagelengthtotal distance (km)average speed (km/h)uphill/downhill hys=8 (m)uphill8enduphill/downhill hys=0 (m)uphill0endaverage temperatureaverage wind speed (m/s)average wind direction (deg)max/min gpx heightmaxgpsheighttotal real energy (kJ)total estimated energy (kJ)average power (W)average estimated power (W)normalized real power (W)normalized estimated power (W)number_of_participantsaverage wind load (1 to -1)cumulative hr intensity scoreavg_heart_rate_cat_orgavg_heart_rate_cat_adjavg slope length (m)
167612752391812M7854.01788830,76909930531725,20057,61,22294169722424216592121821511-0,03550783,633,61500
167613747451812M7854.017864,531,211269136233129,20068,3-1,2158812032140881622432302019-0,06537814,634,211500
167614742251812M7854.017888,836,3321722650151019,20060,65,2215419892448652260412612879-0,17556216,25,821100
167615742651812M7854.0178115,536,9545021970646720,400307,149,22730259524259623062826628019-0,516925465,6214000
167616751741812M7854.017888,129,5924925257258014,20078,32,3014920139298018510341913,633,211600
167617752211812M7854.017884,628,53766432931917,10048-8,6013280124410015310481244,834,31000
167618751701812M7854.0178160,926,859929851524152517,200748,7-37,2026040121007020310830283,913,5321600
167619751811812M7854.017878,830,4316215953153017,40060,23014000150396018410285773,573,181100
167620749191812M7854.01787030,371009528628617,90051,210,4011710141171016410229583,43,021200
167621749911812M7854.017872,426,7439740665166718,200327-18,5010980112608018510237683,042,827300